High-resolution African HLA resource uncovers HLA-DRB1 expression effects underlying vaccine response.

How human genetic variation contributes to vaccine effectiveness in infants is unclear, and data are limited on these relationships in populations with African ancestries. We undertook genetic analyses of vaccine antibody responses in infants from Uganda (n = 1391), Burkina Faso (n = 353) and South Africa (n = 755), identifying associations between human leukocyte antigen (HLA) and antibody response for five of eight tested antigens spanning pertussis, diphtheria and hepatitis B vaccines. In addition, through HLA typing 1,702 individuals from 11 populations of African ancestry derived predominantly from the 1000 Genomes Project, we constructed an imputation resource, fine-mapping class II HLA-DR and DQ associations explaining up to 10% of antibody response variance in our infant cohorts. We observed differences in the genetic architecture of pertussis antibody response between the cohorts with African ancestries and an independent cohort with European ancestry, but found no in silico evidence of differences in HLA peptide binding affinity or breadth. Using immune cell expression quantitative trait loci datasets derived from African-ancestry samples from the 1000 Genomes Project, we found evidence of differential HLA-DRB1 expression correlating with inferred protection from pertussis following vaccination. This work suggests that HLA-DRB1 expression may play a role in vaccine response and should be considered alongside peptide selection to improve vaccine design.

T-cell deficiency and hyperinflammatory monocyte responses associate with Mycobacterium avium complex lung disease.

Immunological mechanisms of susceptibility to nontuberculous mycobacterial (NTM) disease are poorly understood. To understand NTM pathogenesis, we evaluated innate and antigen-specific adaptive immune responses to Mycobacterium avium complex (MAC) in asymptomatic individuals with a previous history of MAC lung disease (MACDZ). We hypothesized that Mav-specific immune responses are associated with susceptibility to MAC lung disease. We measured MAC-, NTM-, or MAC/Mtb-specific T-cell responses by cytokine production, expression of surface markers, and analysis of global gene expression in 27 MACDZ individuals and 32 healthy controls. We also analyzed global gene expression in Mycobacterium avium-infected and uninfected peripheral blood monocytes from 17 MACDZ and 17 healthy controls. We were unable to detect increased T-cell responses against MAC-specific reagents in MACDZ compared to controls, while the responses to non-mycobacteria derived antigens were preserved. MACDZ individuals had a lower frequency of Th1 and Th1* T-cell populations. In addition, MACDZ subjects had lower transcriptional responses in PBMCs stimulated with a mycobacterial peptide pool (MTB300). By contrast, global gene expression analysis demonstrated upregulation of proinflammatory pathways in uninfected and M. avium-infected monocytes, i.e. a hyperinflammatory in vitro response, derived from MACDZ subjects compared to controls. Together, these data suggest a novel immunologic defect which underlies MAC pathogenesis and includes concurrent innate and adaptive dysregulation which persists years after completion of treatment.

Allele-Specific Thresholds of Eluted Ligands for T-Cell Epitope Prediction.

A common strategy for predicting candidate human leukocyte antigen class I T-cell epitopes is to use an affinity-based threshold of 500 nM. Although a 500 nM threshold across alleles effectively identifies most epitopes across alleles, findings showing that major histocompatibility complex repertoire sizes vary by allele indicate that using thresholds specific to individual alleles may improve epitope identification. In this work, we compare different strategies utilizing common and allele-specific thresholds to identify an optimal approach for T-cell epitope prediction. First, we confirmed previous observations that different human leukocyte antigen class I alleles correspond with varying repertoire sizes. Here, we define general and allele-specific thresholds that capture 80% of eluted ligands and a different set of thresholds associated with capturing 9-mer T-cell epitopes at 80% sensitivity. Our analysis revealed that allele-specific threshold performance was roughly equivalent to that of a common threshold when considering a relatively large number of alleles. However, when predicting epitopes for only a few alleles, the use of allele-specific thresholds would be preferable. Finally, we present here for public use a set of allele-specific thresholds that may be used to identify T-cell epitopes at 80% sensitivity.

NetMHCpan-4.1 and NetMHCIIpan-4.0: improved predictions of MHC antigen presentation by concurrent motif deconvolution and integration of MS MHC eluted ligand data.

Major histocompatibility complex (MHC) molecules are expressed on the cell surface, where they present peptides to T cells, which gives them a key role in the development of T-cell immune responses. MHC molecules come in two main variants: MHC Class I (MHC-I) and MHC Class II (MHC-II). MHC-I predominantly present peptides derived from intracellular proteins, whereas MHC-II predominantly presents peptides from extracellular proteins. In both cases, the binding between MHC and antigenic peptides is the most selective step in the antigen presentation pathway. Therefore, the prediction of peptide binding to MHC is a powerful utility to predict the possible specificity of a T-cell immune response. Commonly MHC binding prediction tools are trained on binding affinity or mass spectrometry-eluted ligands. Recent studies have however demonstrated how the integration of both data types can boost predictive performances. Inspired by this, we here present NetMHCpan-4.1 and NetMHCIIpan-4.0, two web servers created to predict binding between peptides and MHC-I and MHC-II, respectively. Both methods exploit tailored machine learning strategies to integrate different training data types, resulting in state-of-the-art performance and outperforming their competitors. The servers are available at http://www.cbs.dtu.dk/services/NetMHCpan-4.1/ and http://www.cbs.dtu.dk/services/NetMHCIIpan-4.0/.

Benchmarking predictions of MHC class I restricted T cell epitopes in a comprehensively studied model system.

T cell epitope candidates are commonly identified using computational prediction tools in order to enable applications such as vaccine design, cancer neoantigen identification, development of diagnostics and removal of unwanted immune responses against protein therapeutics. Most T cell epitope prediction tools are based on machine learning algorithms trained on MHC binding or naturally processed MHC ligand elution data. The ability of currently available tools to predict T cell epitopes has not been comprehensively evaluated. In this study, we used a recently published dataset that systematically defined T cell epitopes recognized in vaccinia virus (VACV) infected C57BL/6 mice (expressing H-2Db and H-2Kb), considering both peptides predicted to bind MHC or experimentally eluted from infected cells, making this the most comprehensive dataset of T cell epitopes mapped in a complex pathogen. We evaluated the performance of all currently publicly available computational T cell epitope prediction tools to identify these major epitopes from all peptides encoded in the VACV proteome. We found that all methods were able to improve epitope identification above random, with the best performance achieved by neural network-based predictions trained on both MHC binding and MHC ligand elution data (NetMHCPan-4.0 and MHCFlurry). Impressively, these methods were able to capture more than half of the major epitopes in the top N = 277 predictions within the N = 767,788 predictions made for distinct peptides of relevant lengths that can theoretically be encoded in the VACV proteome. These performance metrics provide guidance for immunologists as to which prediction methods to use, and what success rates are possible for epitope predictions when considering a highly controlled system of administered immunizations to inbred mice. In addition, this benchmark was implemented in an open and easy to reproduce format, providing developers with a framework for future comparisons against new tools.

IEDB-AR: immune epitope database-analysis resource in 2019.

The Immune Epitope Database Analysis Resource (IEDB-AR, http://tools.iedb.org/) is a companion website to the IEDB that provides computational tools focused on the prediction and analysis of B and T cell epitopes. All of the tools are freely available through the public website and many are also available through a REST API and/or a downloadable command-line tool. A virtual machine image of the entire site is also freely available for non-commercial use and contains most of the tools on the public site. Here, we describe the tools and functionalities that are available in the IEDB-AR, focusing on the 10 new tools that have been added since the last report in the 2012 NAR webserver edition. In addition, many of the tools that were already hosted on the site in 2012 have received updates to newest versions, including NetMHC, NetMHCpan, BepiPred and DiscoTope. Overall, this IEDB-AR update provides a substantial set of updated and novel features for epitope prediction and analysis.

Poor Antigen Processing of Poxvirus Particles Limits CD4+ T Cell Recognition and Impacts Immunogenicity of the Inactivated Vaccine.

CD4+ T cells play critical roles in defending against poxviruses, both by potentiating cellular and humoral responses and by directly killing infected cells. Despite this central role, the basis for pox-specific CD4+ T cell activation, specifically the origin of the poxvirus-derived peptides (epitopes) that activate CD4+ T cells, remains poorly understood. In addition, because the current licensed poxvirus vaccines can cause serious adverse events and even death, elucidating the requirements for MHC class II (MHC-II) processing and presentation of poxviral Ags could be of great use. To address these questions, we explored the CD4+ T cell immunogenicity of ectromelia, the causative agent of mousepox. Having identified a large panel of novel epitopes via a screen of algorithm-selected synthetic peptides, we observed that immunization of mice with inactivated poxvirus primes a virtually undetectable CD4+ T cell response, even when adjuvanted, and is unable to provide protection against disease after a secondary challenge. We postulated that an important contributor to this outcome is the poor processability of whole virions for MHC-II-restricted presentation. In line with this hypothesis, we observed that whole poxvirions are very inefficiently converted into MHC-II-binding peptides by the APC as compared with subviral material. Thus, stability of the virion structure is a critical consideration in the rational design of a safe alternative to the existing live smallpox vaccine.

Major Histocompatibility Complex Binding, Eluted Ligands, and Immunogenicity: Benchmark Testing and Predictions.

Antidrug antibody (ADA) responses impact drug safety, potency, and efficacy. It is generally assumed that ADA responses are associated with human leukocyte antigen (HLA) class II-restricted CD4+ T-cell reactivity. Although this review does not address ADA responses per se, the analysis presented here is relevant to the topic, because measuring or predicting CD4+ T-cell reactivity is a common strategy to address ADA and immunogenicity concerns. Because human CD4+ T-cell reactivity relies on the recognition of peptides bound to HLA class II, prediction, or measurement of the capacity of different peptides to bind or be natural ligands of HLA class II is used as a predictor of CD4+ T-cell reactivity and ADA development. Thus, three different interconnected variables are commonly utilized in predicting T-cell reactivity: major histocompatibility complex (MHC) binding, capacity to be generated as natural HLA ligands, and T-cell immunogenicity. To provide the scientific community with guidance in the relative merit of different approaches, it is necessary to clearly define what outcomes are being considered. Thus, the accuracy of HLA binding predictions varies as a function of what the outcome predicted is, whether it is binding itself, natural processing, or T-cell immunogenicity. Furthermore, it is necessary that the accuracy of prediction is based on rigorous benchmarking, grounded by fair, objective, transparent, and experimental criteria. In this review, we provide our perspective on how different variables and methodologies predict each of the various outcomes and point out knowledge gaps and areas to be addressed by further experimental work.

A Review on T Cell Epitopes Identified Using Prediction and Cell-Mediated Immune Models for Mycobacterium tuberculosis and Bordetella pertussis.

In the present review, we summarize work from our as well as other groups related to the characterization of bacterial T cell epitopes, with a specific focus on two important pathogens, namely, Mycobacterium tuberculosis (Mtb), the bacterium that causes tuberculosis (TB), and Bordetella pertussis (BP), the bacterium that causes whooping cough. Both bacteria and their associated diseases are of large societal significance. Although vaccines exist for both pathogens, their efficacy is incomplete. It is widely thought that defects and/or alteration in T cell compartments are associated with limited vaccine effectiveness. As discussed below, a full genome-wide map was performed in the case of Mtb. For BP, our focus has thus far been on the antigens contained in the acellular vaccine; a full genome-wide screen is in the planning stage. Nevertheless, the sum-total of the results in the two different bacterial systems allows us to exemplify approaches and techniques that we believe are generally applicable to the mapping and characterization of human immune responses to bacterial pathogens. Finally, we add, as a disclaimer, that this review by design is focused on the work produced by our laboratory as an illustration of approaches to the study of T cell responses to Mtb and BP, and is not meant to be comprehensive, nor to detract from the excellent work performed by many other groups.

Footprints of antigen processing boost MHC class II natural ligand predictions.

BACKGROUND: Major histocompatibility complex class II (MHC-II) molecules present peptide fragments to T cells for immune recognition. Current predictors for peptide to MHC-II binding are trained on binding affinity data, generated in vitro and therefore lacking information about antigen processing. METHODS: We generate prediction models of peptide to MHC-II binding trained with naturally eluted ligands derived from mass spectrometry in addition to peptide binding affinity data sets. RESULTS: We show that integrated prediction models incorporate identifiable rules of antigen processing. In fact, we observed detectable signals of protease cleavage at defined positions of the ligands. We also hypothesize a role of the length of the terminal ligand protrusions for trimming the peptide to the MHC presented ligand. CONCLUSIONS: The results of integrating binding affinity and eluted ligand data in a combined model demonstrate improved performance for the prediction of MHC-II ligands and T cell epitopes and foreshadow a new generation of improved peptide to MHC-II prediction tools accounting for the plurality of factors that determine natural presentation of antigens.

Determination of a Predictive Cleavage Motif for Eluted Major Histocompatibility Complex Class II Ligands.

CD4+ T cells have a major role in regulating immune responses. They are activated by recognition of peptides mostly generated from exogenous antigens through the major histocompatibility complex (MHC) class II pathway. Identification of epitopes is important and computational prediction of epitopes is used widely to save time and resources. Although there are algorithms to predict binding affinity of peptides to MHC II molecules, no accurate methods exist to predict which ligands are generated as a result of natural antigen processing. We utilized a dataset of around 14,000 naturally processed ligands identified by mass spectrometry of peptides eluted from MHC class II expressing cells to investigate the existence of sequence signatures potentially related to the cleavage mechanisms that liberate the presented peptides from their source antigens. This analysis revealed preferred amino acids surrounding both N- and C-terminuses of ligands, indicating sequence-specific cleavage preferences. We used these cleavage motifs to develop a method for predicting naturally processed MHC II ligands, and validated that it had predictive power to identify ligands from independent studies. We further confirmed that prediction of ligands based on cleavage motifs could be combined with predictions of MHC binding, and that the combined prediction had superior performance. However, when attempting to predict CD4+ T cell epitopes, either alone or in combination with MHC binding predictions, predictions based on the cleavage motifs did not show predictive power. Given that peptides identified as epitopes based on CD4+ T cell reactivity typically do not have well-defined termini, it is possible that motifs are present but outside of the mapped epitope. Our attempts to take that into account computationally did not show any sign of an increased presence of cleavage motifs around well-characterized CD4+ T cell epitopes. While it is possible that our attempts to translate the cleavage motifs in MHC II ligand elution data into T cell epitope predictions were suboptimal, other possible explanations are that the cleavage signal is too diluted to be detected, or that elution data are enriched for ligands generated through an antigen processing and presentation pathway that is less frequently utilized for T cell epitopes.

Predicting HLA CD4 Immunogenicity in Human Populations.

BACKGROUND: Prediction of T cell immunogenicity is a topic of considerable interest, both in terms of basic understanding of the mechanisms of T cells responses and in terms of practical applications. HLA binding affinity is often used to predict T cell epitopes, since HLA binding affinity is a key requisite for human T cell immunogenicity. However, immunogenicity at the population it is complicated by the high level of variability of HLA molecules, potential other factors beyond HLA as well as the frequent lack of HLA typing data. To overcome those issues, we explored an alternative approach to identify the common characteristics able to distinguish immunogenic peptides from non-recognized peptides. METHODS: Sets of dominant epitopes derived from peer-reviewed published papers were used in conjunction with negative peptides from the same experiments/donors to train neural networks and generate an "immunogenicity score." We also compared the performance of the immunogenicity score with previously described method for immunogenicity prediction based on HLA class II binding at the population level. RESULTS: The immunogenicity score was validated on a series of independent datasets derived from the published literature, representing 57 independent studies where immunogenicity in human populations was assessed by testing overlapping peptides spanning different antigens. Overall, these testing datasets corresponded to over 2,000 peptides and tested in over 1,600 different human donors. The 7-allele method prediction and the immunogenicity score were associated with similar performance [average area under the ROC curve (AUC) values of 0.703 and 0.702, respectively] while the combined methods reached an average AUC of 0.725. This increase in average AUC value is significant compared with the immunogenicity score (p = 0.0135) and a strong trend toward significance is observed when compared to the 7-allele method (p = 0.0938). The new immunogenicity score method is now freely available using CD4 T cell immunogenicity prediction tool on the Immune Epitope Database website (http://tools.iedb.org/CD4episcore). CONCLUSION: The new immunogenicity score predicts CD4 T cell immunogenicity at the population level starting from protein sequences and with no need for HLA typing. Its efficacy has been validated in the context of different antigen sources, ethnicities, and disparate techniques for epitope identification.

Allergen and Epitope Targets of Mouse-Specific T Cell Responses in Allergy and Asthma.

Mouse allergy has become increasingly common, mainly affecting laboratory workers and inner-city households. To date, only one major allergen, namely Mus m 1, has been described. We sought to identify T cell targets in mouse allergic patients. PBMC from allergic donors were expanded with either murine urine or epithelial extract and subsequently screened for cytokine production (IL-5 and IFNγ) in response to overlapping peptides spanning the entire Mus m 1 sequence, peptides from various Mus m 1 isoforms [major urinary proteins (MUPs)], peptides from mouse orthologs of known allergens from other mammalian species and peptides from proteins identified by immunoproteomic analysis of IgE/IgG immunoblots of mouse urine and epithelial extracts. This approach let to the identification of 106 non-redundant T cell epitopes derived from 35 antigens. Three major T cell-activating regions were defined in Mus m 1 alone. Moreover, our data show that immunodominant epitopes were largely shared between Mus m 1 and other MUPs even from different species, suggesting that sequence conservation in different allergens is a determinant for immunodominance. We further identified several novel mouse T cell antigens based on their homology to known mammalian allergens. Analysis of cohort-specific T cell responses revealed that rhinitis and asthmatic patients recognized different epitope repertoires. Epitopes defined herein can be formulated into an epitope "megapool" used to diagnose mouse allergy and study mouse-specific T cell responses directly ex vivo. This analysis of T cell epitopes provides a good basis for future studies to increase our understanding of the immunopathology associated with MO-allergy and asthma.

Correction: Definition of Human Epitopes Recognized in Tetanus Toxoid and Development of an Assay Strategy to Detect Ex Vivo Tetanus CD4+ T Cell Responses.

[This corrects the article DOI: 10.1371/journal.pone.0169086.].

Influenza-derived peptides cross-react with allergens and provide asthma protection.

BACKGROUND: The hygiene hypothesis is the leading concept to explain the current asthma epidemic, which is built on the observation that a lack of bacterial contact early in life induces allergic TH2 immune responses. OBJECTIVE: Because little is known about the contribution of respiratory tract viruses in this context, we evaluated the effect of prior influenza infection on the development of allergic asthma. METHODS: Mice were infected with influenza and, once recovered, subjected to an ovalbumin- or house dust mite-induced experimental asthma protocol. Influenza-polarized effector memory T (Tem) cells were transferred adoptively to allergen-sensitized animals before allergen challenge. A comprehensive in silico analysis assessed homologies between virus- and allergen-derived proteins. Influenza-polarized Tem cells were stimulated ex vivo with candidate peptides. Mice were immunized with a pool of virus-derived T-cell epitopes. RESULTS: In 2 murine models we found a long-lasting preventive effect against experimental asthma features. Protection could be attributed about equally to CD4+ and CD8+ Tem cells from influenza-infected mice. An in silico bioinformatic analysis identified 4 influenza- and 3 allergen-derived MHC class I and MHC class II candidate T-cell epitopes with potential antigen-specific cross-reactivity between influenza and allergens. Lymphocytes from influenza-infected mice produced IFN-γ and IL-2 but not IL-5 on stimulation with the aforementioned peptides. Immunization with a mixture of the influenza peptides conferred asthma protection, and peptide-immunized mice transferred protection through CD4+ and CD8+ Tem cells. CONCLUSION: For the first time, our results illustrate heterologous immunity of virus-infected animals toward allergens. This finding extends the original hygiene hypothesis.

NetMHCpan-4.0: Improved Peptide-MHC Class I Interaction Predictions Integrating Eluted Ligand and Peptide Binding Affinity Data.

Cytotoxic T cells are of central importance in the immune system's response to disease. They recognize defective cells by binding to peptides presented on the cell surface by MHC class I molecules. Peptide binding to MHC molecules is the single most selective step in the Ag-presentation pathway. Therefore, in the quest for T cell epitopes, the prediction of peptide binding to MHC molecules has attracted widespread attention. In the past, predictors of peptide-MHC interactions have primarily been trained on binding affinity data. Recently, an increasing number of MHC-presented peptides identified by mass spectrometry have been reported containing information about peptide-processing steps in the presentation pathway and the length distribution of naturally presented peptides. In this article, we present NetMHCpan-4.0, a method trained on binding affinity and eluted ligand data leveraging the information from both data types. Large-scale benchmarking of the method demonstrates an increase in predictive performance compared with state-of-the-art methods when it comes to identification of naturally processed ligands, cancer neoantigens, and T cell epitopes.

Prior Dengue Virus Exposure Shapes T Cell Immunity to Zika Virus in Humans.

While progress has been made in characterizing humoral immunity to Zika virus (ZIKV) in humans, little is known regarding the corresponding T cell responses to ZIKV. Here, we investigate the kinetics and viral epitopes targeted by T cells responding to ZIKV and address the critical question of whether preexisting dengue virus (DENV) T cell immunity modulates these responses. We find that memory T cell responses elicited by prior infection with DENV or vaccination with tetravalent dengue attenuated vaccines (TDLAV) recognize ZIKV-derived peptides. This cross-reactivity is explained by the sequence similarity of the two viruses, as the ZIKV peptides recognized by DENV-elicited memory T cells are identical or highly conserved in DENV and ZIKV. DENV exposure prior to ZIKV infection also influences the timing and magnitude of the T cell response. ZIKV-reactive T cells in the acute phase of infection are detected earlier and in greater magnitude in DENV-immune patients. Conversely, the frequency of ZIKV-reactive T cells continues to rise in the convalescent phase in DENV-naive donors but declines in DENV-preexposed donors, compatible with more efficient control of ZIKV replication and/or clearance of ZIKV antigen. The quality of responses is also influenced by previous DENV exposure, and ZIKV-specific CD8 T cells from DENV-preexposed donors selectively upregulated granzyme B and PD1, unlike DENV-naive donors. Finally, we discovered that ZIKV structural proteins (E, prM, and C) are major targets of both the CD4 and CD8 T cell responses, whereas DENV T cell epitopes are found primarily in nonstructural proteins.IMPORTANCE The issue of potential ZIKV and DENV cross-reactivity and how preexisting DENV T cell immunity modulates Zika T cell responses is of great relevance, as the two viruses often cocirculate and Zika virus has been spreading in geographical regions where DENV is endemic or hyperendemic. Our data show that memory T cell responses elicited by prior infection with DENV recognize ZIKV-derived peptides and that DENV exposure prior to ZIKV infection influences the timing, magnitude, and quality of the T cell response. Additionally, we show that ZIKV-specific responses target different proteins than DENV-specific responses, pointing toward important implications for vaccine design against this global threat.

Experimental validation of the RATE tool for inferring HLA restrictions of T cell epitopes.

BACKGROUND: The RATE tool was recently developed to computationally infer the HLA restriction of given epitopes from immune response data of HLA typed subjects without additional cumbersome experimentation. RESULTS: Here, RATE was validated using experimentally defined restriction data from a set of 191 tuberculosis-derived epitopes and 63 healthy individuals with MTB infection from the Western Cape Region of South Africa. Using this experimental dataset, the parameters utilized by the RATE tool to infer restriction were optimized, which included relative frequency (RF) of the subjects responding to a given epitope and expressing a given allele as compared to the general test population and the associated p-value in a Fisher's exact test. We also examined the potential for further optimization based on the predicted binding affinity of epitopes to potential restricting HLA alleles, and the absolute number of individuals expressing a given allele and responding to the specific epitope. Different statistical measures, including Matthew's correlation coefficient, accuracy, sensitivity and specificity were used to evaluate performance of RATE as a function of these criteria. Based on our results we recommend selection of HLA restrictions with cutoffs of p-value < 0.01 and RF ≥ 1.3. The usefulness of the tool was demonstrated by inferring new HLA restrictions for epitope sets where restrictions could not be experimentally determined due to lack of necessary cell lines and for an additional data set related to recognition of pollen derived epitopes from allergic patients. CONCLUSIONS: Experimental data sets were used to validate RATE tool and the parameters used by the RATE tool to infer restriction were optimized. New HLA restrictions were identified using the optimized RATE tool.